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Learner Reviews & Feedback for A Complete Reinforcement Learning System (Capstone) by University of Alberta

4.7
stars
620 ratings

About the Course

In this final course, you will put together your knowledge from Courses 1, 2 and 3 to implement a complete RL solution to a problem. This capstone will let you see how each component---problem formulation, algorithm selection, parameter selection and representation design---fits together into a complete solution, and how to make appropriate choices when deploying RL in the real world. This project will require you to implement both the environment to stimulate your problem, and a control agent with Neural Network function approximation. In addition, you will conduct a scientific study of your learning system to develop your ability to assess the robustness of RL agents. To use RL in the real world, it is critical to (a) appropriately formalize the problem as an MDP, (b) select appropriate algorithms, (c ) identify what choices in your implementation will have large impacts on performance and (d) validate the expected behaviour of your algorithms. This capstone is valuable for anyone who is planning on using RL to solve real problems. To be successful in this course, you will need to have completed Courses 1, 2, and 3 of this Specialization or the equivalent. By the end of this course, you will be able to: Complete an RL solution to a problem, starting from problem formulation, appropriate algorithm selection and implementation and empirical study into the effectiveness of the solution....

Top reviews

JJ

Apr 27, 2020

This is the final chapter. It is one of the easiest and it was fun doing that lunar landing project. This specialisation is the best for a person taking baby steps in the reinforcement learning.

CR

Feb 26, 2020

Great course for learning the fundamentals. I liked that it tied into function approximation for deep reinforcement learning. The text book made the fundamental concepts more clear.

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126 - 128 of 128 Reviews for A Complete Reinforcement Learning System (Capstone)

By Naoufel E B

•

Feb 16, 2020

I would have expected to do more of the implementation in the capstone project.

It is still quite useful to go through the details of what is implemented by the instructor for us, but not as effective as having to implement more of it ourselves.

Thanks

By Dustin Z

•

May 5, 2021

A good review of the material covered in the specialization. I would have liked to see a more thorough parameter study and deeper coverage of the environment wrapper around the OpenAI lunar lander environment.

By Manuel V

•

Nov 7, 2019

Not worth it.